ArticleJournal of cardiovascular translational research2017
Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction.
Article in Journal of cardiovascular translational research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Applications of artificial intelligence and computational approaches to imaging for hypertension identification, phenotyping, and outcome prediction: a systematic review.European heart journal. Digital health · 2026Review
- Identification and Validation of an Explainable Predictive Model For Heart Failure in Patients With Hypertension.Reviews in cardiovascular medicine · 2026Article
- Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.European heart journal. Digital health · 2026Article
- Machine learning-based unsupervised phenotypic clustering analysis of patients with IgA nephropathy: Distinct therapeutic responses of different groups.Chinese medical journal · 2026Article
- Unsupervised machine learning for cardiovascular disease: A framework for future studies.European journal of heart failure · 2025Review
- Phenotypes of Patients with Intracerebral Hemorrhage, Complications, and Outcomes.Neurocritical care · 2025Article
- Machine learning in the prevention of heart failure.Heart failure reviews · 2025Review
- Predicting in-hospital mortality among patients admitted with a diagnosis of heart failure: a machine learning approach.ESC heart failure · 2024Review
- Deep learning from latent spatiotemporal information of the heart: Identifying advanced bioimaging markers from echocardiograms.Biophysics reviews · 2024Review
- A machine learning-based score for precise echocardiographic assessment of cardiac remodelling in hypertensive young adults.European heart journal. Imaging methods and practice · 2023Article
- Article
- A cardiologist's guide to machine learning in cardiovascular disease prognosis prediction.Basic research in cardiology · 2023Review
- AI-Aided Disease Prediction in Visualized Medicine.Advances in experimental medicine and biology · 2023Article
- Computational clustering reveals differentiated coronary artery calcium progression at prevalent levels of pulse wave velocity by classifying high-risk patients.Frontiers in cardiovascular medicine · 2023Article
- Phenomapping approach to interpreting coronary dimensions in febrile children.Pediatric investigation · 2022Article
- The Athlete's Heart and Machine Learning: A Review of Current Implementations and Gaps for Future Research.Journal of cardiovascular development and disease · 2022Review
- Multi-Trait Genetic Analysis Reveals Clinically Interpretable Hypertension Subtypes.Circulation. Genomic and precision medicine · 2022Article
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We sought to evaluate whether unbiased machine learning of dense phenotypic data ("phenomapping") could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure with preserved ejection fraction (HFpEF). In the HyperGEN study, a population- and family-based study of hypertension, we studied 1273 hypertensive patients utilizing clinical, laboratory, and conventional echocardiographic phenotyping of the study participants. We used machine learning analysis of 47 continuous phenotypic variables to identify mutually exclusive groups constituting a novel classification of hypertension. The phenomapping analysis classified study participants into 2 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, and indices of cardiac mechanics (e.g., phenogroup #2 had a decreased absolute longitudinal strain [12.8 ± 4.1 vs. 14.6 ± 3.5%] even after adjustment for traditional comorbidities [p < 0.001]). The 2 hypertension phenogroups may represent distinct subtypes that may benefit from targeted therapies for the prevention of HFpEF.
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